Bayesian finite mixture modelling is a flexible parametric modelling approach for classification and density fitting. Many areas of application require distinguishing a signal from a noise component. In practice, it is often difficult to justify a specific distribution for the signal component; therefore, the signal distribution is usually further modelled via a mixture of distributions. However, modelling the signal as a mixture of distributions is computationally non-trivial due to the difficulties in justifying the exact number of components to be used and due to the label switching problem. This paper proposes the use of a non-parametric distribution to model the signal component. We consider the case of discrete data and show how this new methodology leads to more accurate parameter estimation and smaller false non-discovery rate. Moreover, it does not incur the label switching problem. We show an application of the method to data generated by ChIP-sequencing experiments.

Bayesian analysis for mixtures of discrete distributions with a non-parametric component / Alhaji, B. B.; Dai, H.; Hayashi, Y.; Vinciotti, V.; Harrison, A.; Lausen, B.. - In: JOURNAL OF APPLIED STATISTICS. - ISSN 0266-4763. - 43:8(2016), pp. 1369-1385. [10.1080/02664763.2015.1100594]

Bayesian analysis for mixtures of discrete distributions with a non-parametric component

Vinciotti V.;
2016-01-01

Abstract

Bayesian finite mixture modelling is a flexible parametric modelling approach for classification and density fitting. Many areas of application require distinguishing a signal from a noise component. In practice, it is often difficult to justify a specific distribution for the signal component; therefore, the signal distribution is usually further modelled via a mixture of distributions. However, modelling the signal as a mixture of distributions is computationally non-trivial due to the difficulties in justifying the exact number of components to be used and due to the label switching problem. This paper proposes the use of a non-parametric distribution to model the signal component. We consider the case of discrete data and show how this new methodology leads to more accurate parameter estimation and smaller false non-discovery rate. Moreover, it does not incur the label switching problem. We show an application of the method to data generated by ChIP-sequencing experiments.
2016
8
Alhaji, B. B.; Dai, H.; Hayashi, Y.; Vinciotti, V.; Harrison, A.; Lausen, B.
Bayesian analysis for mixtures of discrete distributions with a non-parametric component / Alhaji, B. B.; Dai, H.; Hayashi, Y.; Vinciotti, V.; Harrison, A.; Lausen, B.. - In: JOURNAL OF APPLIED STATISTICS. - ISSN 0266-4763. - 43:8(2016), pp. 1369-1385. [10.1080/02664763.2015.1100594]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/276056
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 1
  • OpenAlex ND
social impact